Progress of Physics-based Mean-field Modeling and Simulation of Steel

The progress of mean-field modeling and simulation in steel is presented. In the modeling, the focus is put on the development and application of a physical modeling base, including Calphad, diffusion assessment, nucleation and growth of precipitates, and dislocation dynamics. This leads to an improved prediction of the materials response after different thermo-mechanical treatments in terms of microstructure evolution and mechanical properties. The presented case studies represent the success of the integrated computational materials engineering approach.

Numerical Modelling of the Powder Metallurgical Manufacturing Chain of High Strength Sintered Gears

This paper presents a digital model for the powder metallurgical (PM) production chain of high-performance sintered gears based on an integrated computational materials engineering (ICME) platform. Discrete and finite element methods (DEM and FEM) were combined to describe the macroscopic material response to the thermomechanical loads and process conditions during the entire production process. The microstructural evolution during the sintering process was predicted on the meso-scale using a Monte-Carlo Model. The effective elastic properties were determined by a homogenization method based on modelling a representative volume element (RVE). The results were subsequently used for the FE modelling of the heat treatment process. Through the development of multi-scale models, it was possible obtain characteristics of the microstructural features. The predicted hardness and residual stress distributions allowed the calculation of the tooth root load bearing capacity of the heat-treated sintered gears.

Optimization of Magnesium Alloy Casting Process: An Integrated Computational Materials Engineering (ICME) Approach

Traditional casting process optimization usually adopts empirical trial and error method. Process parameters were modified repeatedly within a certain range until a satisfactory solution is obtained, which is costly and inefficient. Therefore, based on integrated computational materials engineering, Magnesium Alloy Simulation Integrated Platform (MASIP) was constructed. MASIP completed the automatic operation of the entire simulation process from the CAD model data input to the process-microstructure-performance. It realized the rapid optimization simulation prediction of process-microstructure-performance, and solved the problems of long cycle and low efficiency of traditional process optimization. This paper studied the low-pressure casting optimization process of magnesium alloy thin-walled cylindrical parts based on MASIP. The calculation took casting temperature, mold temperature and holding pressure as the optimized variables, and the yield strength of the casting as the target variable. The experimental results showed that MASIP can fairly complete the structure simulation and performance prediction of castings, greatly reduce the time cost of the calculation process, and improve the efficiency of process optimization.

Multiphysics Integrated Computational Materials Engineering Linking Additive Manufacturing Process Parameters with Part Performance

The central goal of this chapter is to present an outline of the plan and current status of an effort to connect Additive Manufacturing (AM) process parameters with parameters describing the functional performance of produced parts. The term “functional performance” here represents primarily mechanical or thermal or electrochemical performance. The described effort represents an overview of the main research activities within a new multi-year grand-challenge project initiated at the US Naval Research Laboratory (US-NRL) in late 2016, in collaboration with groups from various academic institutions.

Recent Advances in the Prediction of Weld Residual Stress and Distortion - Part 1

Residual stresses and distortions are the result of complex interactions between welding heat input, the material’s high-temperature response, and joint constraint conditions. Both weld residual stress and distortion can significantly impair the performance and reliability of welded structures. In the past two decades, there have been many significant and exciting developments in the prediction and mitigation of weld residual stress and distortion. This paper reviews the recent advances in the prediction of weld residual stress and distortion by focusing on the numerical modeling theory and methods. The prediction methods covered in this paper include a thermo-mechanical-metallurgical method, simplified analysis methods, friction stir welding modeling methods, buckling distortion prediction methods, a welding cloud computational method, integrated manufacturing process modeling, and integrated computational materials engineering (ICME) weld modeling. Remaining challenges and new developments are also discussed to guide future predictions of weld residual stress and distortion.

An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built with Additive Manufacturing

Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for Additive Manufacturing (DC-AM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DC-AM provides an efficient capability to more fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.